added citation to readme

README.md

@@ -8,5 +8,21 @@ Data includes:
 - [Train](train) and [test](data/test/smm4h) data for SMM4H 2022 Task 2: tweets annotated for stance and premise prediction on three claims about COVID-19 mandates such as stay-at-home-orders, school closures, and face masks
 - [2070](test/vaccine_tweets) annotated tweets about vaccine mandates, that were not used in the official SMM4H competition
 - [600](test/vaccine_tweets/unused) annotated tweets about vaccine mandates with low inter-annotators agreement.
-
+## Citation
+If you find this dataset useful, please cite:
+```
+@inproceedings{davydova-tutubalina-2022-smm4h,
+    title = "{SMM}4{H} 2022 Task 2: Dataset for stance and premise detection in tweets about health mandates related to {COVID}-19",
+    author = "Davydova, Vera  and
+      Tutubalina, Elena",
+    booktitle = "Proceedings of The Seventh Workshop on Social Media Mining for Health Applications, Workshop {\&} Shared Task",
+    month = oct,
+    year = "2022",
+    address = "Gyeongju, Republic of Korea",
+    publisher = "Association for Computational Linguistics",
+    url = "https://aclanthology.org/2022.smm4h-1.53",
+    pages = "216--220",
+    abstract = "This paper is an organizers{'} report of the competition on argument mining systems dealing with English tweets about COVID-19 health mandates. This competition was held within the framework of the SMM4H 2022 shared tasks. During the competition, the participants were offered two subtasks: stance detection and premise classification. We present a manually annotated corpus containing 6,156 short posts from Twitter on three topics related to the COVID-19 pandemic: school closures, stay-at-home orders, and wearing masks. We hope the prepared dataset will support further research on argument mining in the health field.",
+}
+```
 <img width="1190" alt="smm4h_graphical_abstract" src="https://github.com/Veranchos/ArgMining_tweets/assets/37894718/44f183ea-b17c-4afc-a7b8-32b35a963c2c">